suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(forcats))
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(gapminder))

Part 1: Factor management

Factors are how categorical data are stored. The values a factor can take on are called levels. It is important to check variable types. What you think are characters may actually be stored numerically. Let’s explore the data frame.

summary(gapminder)

##         country        continent        year         lifeExp     
##  Afghanistan:  12   Africa  :624   Min.   :1952   Min.   :23.60  
##  Albania    :  12   Americas:300   1st Qu.:1966   1st Qu.:48.20  
##  Algeria    :  12   Asia    :396   Median :1980   Median :60.71  
##  Angola     :  12   Europe  :360   Mean   :1980   Mean   :59.47  
##  Argentina  :  12   Oceania : 24   3rd Qu.:1993   3rd Qu.:70.85  
##  Australia  :  12                  Max.   :2007   Max.   :82.60  
##  (Other)    :1632                                                
##       pop              gdpPercap       
##  Min.   :6.001e+04   Min.   :   241.2  
##  1st Qu.:2.794e+06   1st Qu.:  1202.1  
##  Median :7.024e+06   Median :  3531.8  
##  Mean   :2.960e+07   Mean   :  7215.3  
##  3rd Qu.:1.959e+07   3rd Qu.:  9325.5  
##  Max.   :1.319e+09   Max.   :113523.1  
## 

Before beginning the factor management exercise, let’s get to know the factors in the gapminder data set.

class(gapminder$country)

## [1] "factor"

class(gapminder$continent)

## [1] "factor"

class(gapminder$year)

## [1] "integer"

class(gapminder$lifeExp)

## [1] "numeric"

class(gapminder$pop)

## [1] "integer"

class(gapminder$gdpPercap)

## [1] "numeric"

We see that we have two factors in this data set. Let’s see the levels for “country” and “continent” before we begin filtering the data.

nlevels(gapminder$country)

## [1] 142

nlevels(gapminder$continent)

## [1] 5

levels(gapminder$country)

##   [1] "Afghanistan"              "Albania"                 
##   [3] "Algeria"                  "Angola"                  
##   [5] "Argentina"                "Australia"               
##   [7] "Austria"                  "Bahrain"                 
##   [9] "Bangladesh"               "Belgium"                 
##  [11] "Benin"                    "Bolivia"                 
##  [13] "Bosnia and Herzegovina"   "Botswana"                
##  [15] "Brazil"                   "Bulgaria"                
##  [17] "Burkina Faso"             "Burundi"                 
##  [19] "Cambodia"                 "Cameroon"                
##  [21] "Canada"                   "Central African Republic"
##  [23] "Chad"                     "Chile"                   
##  [25] "China"                    "Colombia"                
##  [27] "Comoros"                  "Congo, Dem. Rep."        
##  [29] "Congo, Rep."              "Costa Rica"              
##  [31] "Cote d'Ivoire"            "Croatia"                 
##  [33] "Cuba"                     "Czech Republic"          
##  [35] "Denmark"                  "Djibouti"                
##  [37] "Dominican Republic"       "Ecuador"                 
##  [39] "Egypt"                    "El Salvador"             
##  [41] "Equatorial Guinea"        "Eritrea"                 
##  [43] "Ethiopia"                 "Finland"                 
##  [45] "France"                   "Gabon"                   
##  [47] "Gambia"                   "Germany"                 
##  [49] "Ghana"                    "Greece"                  
##  [51] "Guatemala"                "Guinea"                  
##  [53] "Guinea-Bissau"            "Haiti"                   
##  [55] "Honduras"                 "Hong Kong, China"        
##  [57] "Hungary"                  "Iceland"                 
##  [59] "India"                    "Indonesia"               
##  [61] "Iran"                     "Iraq"                    
##  [63] "Ireland"                  "Israel"                  
##  [65] "Italy"                    "Jamaica"                 
##  [67] "Japan"                    "Jordan"                  
##  [69] "Kenya"                    "Korea, Dem. Rep."        
##  [71] "Korea, Rep."              "Kuwait"                  
##  [73] "Lebanon"                  "Lesotho"                 
##  [75] "Liberia"                  "Libya"                   
##  [77] "Madagascar"               "Malawi"                  
##  [79] "Malaysia"                 "Mali"                    
##  [81] "Mauritania"               "Mauritius"               
##  [83] "Mexico"                   "Mongolia"                
##  [85] "Montenegro"               "Morocco"                 
##  [87] "Mozambique"               "Myanmar"                 
##  [89] "Namibia"                  "Nepal"                   
##  [91] "Netherlands"              "New Zealand"             
##  [93] "Nicaragua"                "Niger"                   
##  [95] "Nigeria"                  "Norway"                  
##  [97] "Oman"                     "Pakistan"                
##  [99] "Panama"                   "Paraguay"                
## [101] "Peru"                     "Philippines"             
## [103] "Poland"                   "Portugal"                
## [105] "Puerto Rico"              "Reunion"                 
## [107] "Romania"                  "Rwanda"                  
## [109] "Sao Tome and Principe"    "Saudi Arabia"            
## [111] "Senegal"                  "Serbia"                  
## [113] "Sierra Leone"             "Singapore"               
## [115] "Slovak Republic"          "Slovenia"                
## [117] "Somalia"                  "South Africa"            
## [119] "Spain"                    "Sri Lanka"               
## [121] "Sudan"                    "Swaziland"               
## [123] "Sweden"                   "Switzerland"             
## [125] "Syria"                    "Taiwan"                  
## [127] "Tanzania"                 "Thailand"                
## [129] "Togo"                     "Trinidad and Tobago"     
## [131] "Tunisia"                  "Turkey"                  
## [133] "Uganda"                   "United Kingdom"          
## [135] "United States"            "Uruguay"                 
## [137] "Venezuela"                "Vietnam"                 
## [139] "West Bank and Gaza"       "Yemen, Rep."             
## [141] "Zambia"                   "Zimbabwe"

levels(gapminder$continent)

## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

Now let’s work towards dropping “Oceoania”.

We will start by filtering the gapminder data to remove observations associated with the continent of Oceania.

No_Oceania <- gapminder %>%
  filter(continent == "Africa" | continent =="Americas"|continent == "Asia"|continent == "Europe")
No_Oceania %>% 
  sample_frac(0.1) %>% #just showing a sample of the data
  knitr:: kable(format = "markdown", justify = "centre")

country	continent	year	lifeExp	pop	gdpPercap
Czech Republic	Europe	2002	75.510	10256295	17596.2102
Korea, Dem. Rep.	Asia	1982	69.100	17647518	4106.5253
Paraguay	Americas	2002	70.755	5884491	3783.6742
Angola	Africa	1977	39.483	6162675	3008.6474
Uganda	Africa	1962	45.344	7688797	767.2717
Djibouti	Africa	1967	42.074	127617	3020.0505
Bangladesh	Asia	1992	56.018	113704579	837.8102
Switzerland	Europe	1982	76.210	6468126	28397.7151
Belgium	Europe	1952	68.000	8730405	8343.1051
Brazil	Americas	1967	57.632	88049823	3429.8644
Eritrea	Africa	2002	55.240	4414865	765.3500
Albania	Europe	1977	68.930	2509048	3533.0039
India	Asia	1957	40.249	409000000	590.0620
Angola	Africa	1992	40.647	8735988	2627.8457
Montenegro	Europe	1992	75.435	621621	7003.3390
Congo, Dem. Rep.	Africa	1972	45.989	23007669	904.8961
Denmark	Europe	1962	72.350	4646899	13583.3135
Central African Republic	Africa	1962	39.475	1523478	1193.0688
Congo, Dem. Rep.	Africa	1992	45.548	41672143	457.7192
Netherlands	Europe	2007	79.762	16570613	36797.9333
Malawi	Africa	1962	38.410	3628608	427.9011
United Kingdom	Europe	1967	71.360	54959000	14142.8509
Eritrea	Africa	1977	44.535	2512642	505.7538
Guatemala	Americas	1997	66.322	9803875	4684.3138
Mauritius	Africa	1962	60.246	701016	2529.0675
Trinidad and Tobago	Americas	1952	59.100	662850	3023.2719
Bosnia and Herzegovina	Europe	1967	64.790	3585000	2172.3524
Austria	Europe	1987	74.940	7578903	23687.8261
Botswana	Africa	1952	47.622	442308	851.2411
Colombia	Americas	2002	71.682	41008227	5755.2600
Austria	Europe	1977	72.170	7568430	19749.4223
Benin	Africa	2007	56.728	8078314	1441.2849
Austria	Europe	1952	66.800	6927772	6137.0765
Serbia	Europe	1972	68.700	8313288	10522.0675
Nicaragua	Americas	1972	55.151	2182908	4688.5933
Mongolia	Asia	1962	48.251	1010280	1056.3540
Namibia	Africa	1997	58.909	1774766	3899.5243
Norway	Europe	1972	74.340	3933004	18965.0555
Costa Rica	Americas	1992	75.713	3173216	6160.4163
Morocco	Africa	1967	50.335	14770296	1711.0448
Austria	Europe	1982	73.180	7574613	21597.0836
Burkina Faso	Africa	1992	50.260	8878303	931.7528
United Kingdom	Europe	1992	76.420	57866349	22705.0925
Angola	Africa	2002	41.003	10866106	2773.2873
Morocco	Africa	1987	62.677	22987397	2755.0470
Taiwan	Asia	1952	58.500	8550362	1206.9479
El Salvador	Americas	1967	55.855	3232927	4358.5954
El Salvador	Americas	1987	63.154	4842194	4140.4421
Morocco	Africa	1952	42.873	9939217	1688.2036
Sri Lanka	Asia	2002	70.815	19576783	3015.3788
Hong Kong, China	Asia	1982	75.450	5264500	14560.5305
Slovenia	Europe	1977	70.970	1746919	15277.0302
Austria	Europe	2007	79.829	8199783	36126.4927
Somalia	Africa	1957	34.977	2780415	1258.1474
Brazil	Americas	1972	59.504	100840058	4985.7115
Central African Republic	Africa	1997	46.066	3696513	740.5063
Swaziland	Africa	1967	46.633	420690	2613.1017
Afghanistan	Asia	1972	36.088	13079460	739.9811
Indonesia	Asia	1997	66.041	199278000	3119.3356
Trinidad and Tobago	Americas	2002	68.976	1101832	11460.6002
Bahrain	Asia	2007	75.635	708573	29796.0483
Romania	Europe	1967	66.800	19284814	6470.8665
Slovak Republic	Europe	1962	70.330	4237384	7481.1076
Trinidad and Tobago	Americas	1967	65.400	960155	5621.3685
Panama	Americas	2002	74.712	2990875	7356.0319
Jamaica	Americas	1957	62.610	1535090	4756.5258
Guinea	Africa	2007	56.007	9947814	942.6542
Hong Kong, China	Asia	1967	70.000	3722800	6197.9628
Lebanon	Asia	1997	70.265	3430388	8754.9639
Benin	Africa	1982	50.904	3641603	1277.8976
Libya	Africa	1972	52.773	2183877	21011.4972
Malaysia	Asia	1962	55.737	8906385	2036.8849
Hong Kong, China	Asia	1977	73.600	4583700	11186.1413
Lebanon	Asia	1992	69.292	3219994	6890.8069
Switzerland	Europe	1992	78.030	6995447	31871.5303
Guatemala	Americas	1957	44.142	3640876	2617.1560
Niger	Africa	1962	39.487	4076008	997.7661
Uganda	Africa	2002	47.813	24739869	927.7210
Jamaica	Americas	1952	58.530	1426095	2898.5309
Libya	Africa	1962	47.808	1441863	6757.0308
Swaziland	Africa	1987	57.678	779348	3984.8398
Ecuador	Americas	1952	48.357	3548753	3522.1107
Central African Republic	Africa	1977	46.775	2167533	1109.3743
Hong Kong, China	Asia	1957	64.750	2736300	3629.0765
Israel	Asia	2002	79.696	6029529	21905.5951
Saudi Arabia	Asia	1967	49.901	5618198	16903.0489
Mongolia	Asia	2007	66.803	2874127	3095.7723
Cameroon	Africa	1957	40.428	5359923	1313.0481
Togo	Africa	1982	55.471	2644765	1344.5780
Germany	Europe	1957	69.100	71019069	10187.8267
Slovak Republic	Europe	1992	71.380	5302888	9498.4677
Guinea	Africa	1977	40.762	4227026	874.6859
Nigeria	Africa	1997	47.464	106207839	1624.9413
Poland	Europe	1952	61.310	25730551	4029.3297
Niger	Africa	1997	51.313	9666252	580.3052
Ethiopia	Africa	2002	50.725	67946797	530.0535
Kuwait	Asia	1977	69.343	1140357	59265.4771
Burkina Faso	Africa	1997	50.324	10352843	946.2950
Kuwait	Asia	1992	75.190	1418095	34932.9196
Comoros	Africa	2007	65.152	710960	986.1479
Israel	Asia	1972	71.630	3095893	12786.9322
Venezuela	Americas	1962	60.770	8143375	8422.9742
Honduras	Americas	2007	70.198	7483763	3548.3308
Jordan	Asia	1992	68.015	3867409	3431.5936
Tanzania	Africa	1992	50.440	26605473	825.6825
Panama	Americas	1962	61.817	1215725	3536.5403
Gambia	Africa	1987	49.265	848406	611.6589
Croatia	Europe	2002	74.876	4481020	11628.3890
Somalia	Africa	1992	39.658	6099799	926.9603
Nigeria	Africa	2002	46.608	119901274	1615.2864
Canada	Americas	1952	68.750	14785584	11367.1611
China	Asia	1982	65.525	1000281000	962.4214
Spain	Europe	1957	66.660	29841614	4564.8024
Haiti	Americas	1992	55.089	6326682	1456.3095
Swaziland	Africa	1992	58.474	962344	3553.0224
Djibouti	Africa	1972	44.366	178848	3694.2124
Nigeria	Africa	2007	46.859	135031164	2013.9773
Swaziland	Africa	2007	39.613	1133066	4513.4806
Namibia	Africa	2007	52.906	2055080	4811.0604
Slovenia	Europe	1997	75.130	2011612	17161.1073
Montenegro	Europe	1972	70.636	527678	7778.4140
Uruguay	Americas	1992	72.752	3149262	8137.0048
Dominican Republic	Americas	1967	56.751	4049146	1653.7230
Equatorial Guinea	Africa	1997	48.245	439971	2814.4808
Turkey	Europe	1977	59.507	42404033	4269.1223
Bolivia	Americas	1967	45.032	4040665	2586.8861
Mexico	Americas	1967	60.110	47995559	5754.7339
Cambodia	Asia	1962	43.415	6083619	496.9136
Montenegro	Europe	2002	73.981	720230	6557.1943
Swaziland	Africa	1997	54.289	1054486	3876.7685
Bahrain	Asia	1987	70.750	454612	18524.0241
Hong Kong, China	Asia	1952	60.960	2125900	3054.4212
Syria	Asia	1972	57.296	6701172	2571.4230
Congo, Rep.	Africa	1997	52.962	2800947	3484.1644
Denmark	Europe	1987	74.800	5127024	25116.1758
Saudi Arabia	Asia	1992	68.768	16945857	24841.6178
Cote d’Ivoire	Africa	1982	53.983	9025951	2602.7102
Lesotho	Africa	1992	59.685	1803195	977.4863
Mauritius	Africa	1982	66.711	992040	3688.0377
Yemen, Rep.	Asia	1977	44.175	8403990	1829.7652
Sierra Leone	Africa	1957	31.570	2295678	1004.4844
Burundi	Africa	1997	45.326	6121610	463.1151
Uruguay	Americas	2007	76.384	3447496	10611.4630
Singapore	Asia	1972	69.521	2152400	8597.7562
Madagascar	Africa	1997	54.978	14165114	986.2959
South Africa	Africa	1962	49.951	18356657	5768.7297
Panama	Americas	1972	66.216	1616384	5364.2497
Portugal	Europe	1962	64.390	9019800	4727.9549
Colombia	Americas	2007	72.889	44227550	7006.5804
Canada	Americas	1992	77.950	28523502	26342.8843
Iceland	Europe	1952	72.490	147962	7267.6884
Spain	Europe	1972	73.060	34513161	10638.7513
Sierra Leone	Africa	1952	30.331	2143249	879.7877
Pakistan	Asia	1967	49.800	60641899	942.4083
Italy	Europe	1987	76.420	56729703	19207.2348
Ethiopia	Africa	1972	43.515	30770372	566.2439
Ghana	Africa	1997	58.556	18418288	1005.2458
Puerto Rico	Americas	1977	73.440	3080828	9770.5249
United Kingdom	Europe	1957	70.420	51430000	11283.1779
Costa Rica	Americas	1967	65.424	1588717	4161.7278
United States	Americas	1982	74.650	232187835	25009.5591
Sweden	Europe	1967	74.160	7867931	15258.2970
Cambodia	Asia	1997	56.534	11782962	734.2852
Burundi	Africa	1977	45.910	3834415	556.1033
United Kingdom	Europe	1997	77.218	58808266	26074.5314
Dominican Republic	Americas	1977	61.788	5302800	2681.9889
United Kingdom	Europe	1952	69.180	50430000	9979.5085
Syria	Asia	2002	73.053	17155814	4090.9253

Let’s check how many levels there are and whether we need to remove unused factor levels.

nlevels(No_Oceania$continent)

## [1] 5

levels(No_Oceania$continent)

## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

We see that there are still 5 levels. Let’s remove the unused factor levels.

Drop_Oceania <- No_Oceania %>% 
  droplevels()
nlevels(Drop_Oceania$continent)

## [1] 4

levels(Drop_Oceania$continent)

## [1] "Africa"   "Americas" "Asia"     "Europe"

We do note that the factor levels are ordered alphabetically. Let’s use forcats to re-order the factor levels. We can re-order in different ways.

One way to re-order is by frequency.For example, the frequency of continents in the data.

Drop_Oceania$continent %>% 
  fct_infreq() %>% 
  levels()

## [1] "Africa"   "Asia"     "Europe"   "Americas"

We can also re-order by the value of other variables in the data as life expectanc or gdp.

Drop_Oceania_1980 <- Drop_Oceania %>% 
  filter(year > 1979) %>% 
  group_by(continent, year) %>% 
  mutate(mediangdp = median(gdpPercap))

Drop_Oceania_1980

## # A tibble: 840 x 7
## # Groups:   continent, year [24]
##    country     continent  year lifeExp      pop gdpPercap mediangdp
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>     <dbl>
##  1 Afghanistan Asia       1982    39.9 12881816      978.     4107.
##  2 Afghanistan Asia       1987    40.8 13867957      852.     4106.
##  3 Afghanistan Asia       1992    41.7 16317921      649.     3726.
##  4 Afghanistan Asia       1997    41.8 22227415      635.     3645.
##  5 Afghanistan Asia       2002    42.1 25268405      727.     4091.
##  6 Afghanistan Asia       2007    43.8 31889923      975.     4471.
##  7 Albania     Europe     1982    70.4  2780097     3631.    15323.
##  8 Albania     Europe     1987    72    3075321     3739.    16215.
##  9 Albania     Europe     1992    71.6  3326498     2497.    17550.
## 10 Albania     Europe     1997    73.0  3428038     3193.    19596.
## # ... with 830 more rows

fct_reorder(Drop_Oceania_1980$continent, Drop_Oceania_1980$gdpPercap, min) %>% 
  levels() %>% head()

## [1] "Africa"   "Asia"     "Americas" "Europe"

Now we can put this into graph form.

ggplot(Drop_Oceania_1980, aes(x = year, y = Drop_Oceania_1980$mediangdp, color = fct_reorder2(continent, year, Drop_Oceania_1980$mediangdp))) +
  geom_line() +
  labs(color = "Continent")+
  xlab("Year") + ylab("Median GDP") +
  ggtitle("Median GDP 1980-2007 by Continent")

We see that reordering the levels allows the legend to be organized in the same fashion as the trendlines.

Part 2 File input and output

Let’s try saving some wrangled data into a new file.

trial<- gapminder %>%
  filter(year == "2007")

write.csv(trial, file = "assignment5_stat545")

Now let’s import this file to see if we can read it.

read.csv("assignment5_stat545")

##       X                  country continent year lifeExp        pop
## 1     1              Afghanistan      Asia 2007  43.828   31889923
## 2     2                  Albania    Europe 2007  76.423    3600523
## 3     3                  Algeria    Africa 2007  72.301   33333216
## 4     4                   Angola    Africa 2007  42.731   12420476
## 5     5                Argentina  Americas 2007  75.320   40301927
## 6     6                Australia   Oceania 2007  81.235   20434176
## 7     7                  Austria    Europe 2007  79.829    8199783
## 8     8                  Bahrain      Asia 2007  75.635     708573
## 9     9               Bangladesh      Asia 2007  64.062  150448339
## 10   10                  Belgium    Europe 2007  79.441   10392226
## 11   11                    Benin    Africa 2007  56.728    8078314
## 12   12                  Bolivia  Americas 2007  65.554    9119152
## 13   13   Bosnia and Herzegovina    Europe 2007  74.852    4552198
## 14   14                 Botswana    Africa 2007  50.728    1639131
## 15   15                   Brazil  Americas 2007  72.390  190010647
## 16   16                 Bulgaria    Europe 2007  73.005    7322858
## 17   17             Burkina Faso    Africa 2007  52.295   14326203
## 18   18                  Burundi    Africa 2007  49.580    8390505
## 19   19                 Cambodia      Asia 2007  59.723   14131858
## 20   20                 Cameroon    Africa 2007  50.430   17696293
## 21   21                   Canada  Americas 2007  80.653   33390141
## 22   22 Central African Republic    Africa 2007  44.741    4369038
## 23   23                     Chad    Africa 2007  50.651   10238807
## 24   24                    Chile  Americas 2007  78.553   16284741
## 25   25                    China      Asia 2007  72.961 1318683096
## 26   26                 Colombia  Americas 2007  72.889   44227550
## 27   27                  Comoros    Africa 2007  65.152     710960
## 28   28         Congo, Dem. Rep.    Africa 2007  46.462   64606759
## 29   29              Congo, Rep.    Africa 2007  55.322    3800610
## 30   30               Costa Rica  Americas 2007  78.782    4133884
## 31   31            Cote d'Ivoire    Africa 2007  48.328   18013409
## 32   32                  Croatia    Europe 2007  75.748    4493312
## 33   33                     Cuba  Americas 2007  78.273   11416987
## 34   34           Czech Republic    Europe 2007  76.486   10228744
## 35   35                  Denmark    Europe 2007  78.332    5468120
## 36   36                 Djibouti    Africa 2007  54.791     496374
## 37   37       Dominican Republic  Americas 2007  72.235    9319622
## 38   38                  Ecuador  Americas 2007  74.994   13755680
## 39   39                    Egypt    Africa 2007  71.338   80264543
## 40   40              El Salvador  Americas 2007  71.878    6939688
## 41   41        Equatorial Guinea    Africa 2007  51.579     551201
## 42   42                  Eritrea    Africa 2007  58.040    4906585
## 43   43                 Ethiopia    Africa 2007  52.947   76511887
## 44   44                  Finland    Europe 2007  79.313    5238460
## 45   45                   France    Europe 2007  80.657   61083916
## 46   46                    Gabon    Africa 2007  56.735    1454867
## 47   47                   Gambia    Africa 2007  59.448    1688359
## 48   48                  Germany    Europe 2007  79.406   82400996
## 49   49                    Ghana    Africa 2007  60.022   22873338
## 50   50                   Greece    Europe 2007  79.483   10706290
## 51   51                Guatemala  Americas 2007  70.259   12572928
## 52   52                   Guinea    Africa 2007  56.007    9947814
## 53   53            Guinea-Bissau    Africa 2007  46.388    1472041
## 54   54                    Haiti  Americas 2007  60.916    8502814
## 55   55                 Honduras  Americas 2007  70.198    7483763
## 56   56         Hong Kong, China      Asia 2007  82.208    6980412
## 57   57                  Hungary    Europe 2007  73.338    9956108
## 58   58                  Iceland    Europe 2007  81.757     301931
## 59   59                    India      Asia 2007  64.698 1110396331
## 60   60                Indonesia      Asia 2007  70.650  223547000
## 61   61                     Iran      Asia 2007  70.964   69453570
## 62   62                     Iraq      Asia 2007  59.545   27499638
## 63   63                  Ireland    Europe 2007  78.885    4109086
## 64   64                   Israel      Asia 2007  80.745    6426679
## 65   65                    Italy    Europe 2007  80.546   58147733
## 66   66                  Jamaica  Americas 2007  72.567    2780132
## 67   67                    Japan      Asia 2007  82.603  127467972
## 68   68                   Jordan      Asia 2007  72.535    6053193
## 69   69                    Kenya    Africa 2007  54.110   35610177
## 70   70         Korea, Dem. Rep.      Asia 2007  67.297   23301725
## 71   71              Korea, Rep.      Asia 2007  78.623   49044790
## 72   72                   Kuwait      Asia 2007  77.588    2505559
## 73   73                  Lebanon      Asia 2007  71.993    3921278
## 74   74                  Lesotho    Africa 2007  42.592    2012649
## 75   75                  Liberia    Africa 2007  45.678    3193942
## 76   76                    Libya    Africa 2007  73.952    6036914
## 77   77               Madagascar    Africa 2007  59.443   19167654
## 78   78                   Malawi    Africa 2007  48.303   13327079
## 79   79                 Malaysia      Asia 2007  74.241   24821286
## 80   80                     Mali    Africa 2007  54.467   12031795
## 81   81               Mauritania    Africa 2007  64.164    3270065
## 82   82                Mauritius    Africa 2007  72.801    1250882
## 83   83                   Mexico  Americas 2007  76.195  108700891
## 84   84                 Mongolia      Asia 2007  66.803    2874127
## 85   85               Montenegro    Europe 2007  74.543     684736
## 86   86                  Morocco    Africa 2007  71.164   33757175
## 87   87               Mozambique    Africa 2007  42.082   19951656
## 88   88                  Myanmar      Asia 2007  62.069   47761980
## 89   89                  Namibia    Africa 2007  52.906    2055080
## 90   90                    Nepal      Asia 2007  63.785   28901790
## 91   91              Netherlands    Europe 2007  79.762   16570613
## 92   92              New Zealand   Oceania 2007  80.204    4115771
## 93   93                Nicaragua  Americas 2007  72.899    5675356
## 94   94                    Niger    Africa 2007  56.867   12894865
## 95   95                  Nigeria    Africa 2007  46.859  135031164
## 96   96                   Norway    Europe 2007  80.196    4627926
## 97   97                     Oman      Asia 2007  75.640    3204897
## 98   98                 Pakistan      Asia 2007  65.483  169270617
## 99   99                   Panama  Americas 2007  75.537    3242173
## 100 100                 Paraguay  Americas 2007  71.752    6667147
## 101 101                     Peru  Americas 2007  71.421   28674757
## 102 102              Philippines      Asia 2007  71.688   91077287
## 103 103                   Poland    Europe 2007  75.563   38518241
## 104 104                 Portugal    Europe 2007  78.098   10642836
## 105 105              Puerto Rico  Americas 2007  78.746    3942491
## 106 106                  Reunion    Africa 2007  76.442     798094
## 107 107                  Romania    Europe 2007  72.476   22276056
## 108 108                   Rwanda    Africa 2007  46.242    8860588
## 109 109    Sao Tome and Principe    Africa 2007  65.528     199579
## 110 110             Saudi Arabia      Asia 2007  72.777   27601038
## 111 111                  Senegal    Africa 2007  63.062   12267493
## 112 112                   Serbia    Europe 2007  74.002   10150265
## 113 113             Sierra Leone    Africa 2007  42.568    6144562
## 114 114                Singapore      Asia 2007  79.972    4553009
## 115 115          Slovak Republic    Europe 2007  74.663    5447502
## 116 116                 Slovenia    Europe 2007  77.926    2009245
## 117 117                  Somalia    Africa 2007  48.159    9118773
## 118 118             South Africa    Africa 2007  49.339   43997828
## 119 119                    Spain    Europe 2007  80.941   40448191
## 120 120                Sri Lanka      Asia 2007  72.396   20378239
## 121 121                    Sudan    Africa 2007  58.556   42292929
## 122 122                Swaziland    Africa 2007  39.613    1133066
## 123 123                   Sweden    Europe 2007  80.884    9031088
## 124 124              Switzerland    Europe 2007  81.701    7554661
## 125 125                    Syria      Asia 2007  74.143   19314747
## 126 126                   Taiwan      Asia 2007  78.400   23174294
## 127 127                 Tanzania    Africa 2007  52.517   38139640
## 128 128                 Thailand      Asia 2007  70.616   65068149
## 129 129                     Togo    Africa 2007  58.420    5701579
## 130 130      Trinidad and Tobago  Americas 2007  69.819    1056608
## 131 131                  Tunisia    Africa 2007  73.923   10276158
## 132 132                   Turkey    Europe 2007  71.777   71158647
## 133 133                   Uganda    Africa 2007  51.542   29170398
## 134 134           United Kingdom    Europe 2007  79.425   60776238
## 135 135            United States  Americas 2007  78.242  301139947
## 136 136                  Uruguay  Americas 2007  76.384    3447496
## 137 137                Venezuela  Americas 2007  73.747   26084662
## 138 138                  Vietnam      Asia 2007  74.249   85262356
## 139 139       West Bank and Gaza      Asia 2007  73.422    4018332
## 140 140              Yemen, Rep.      Asia 2007  62.698   22211743
## 141 141                   Zambia    Africa 2007  42.384   11746035
## 142 142                 Zimbabwe    Africa 2007  43.487   12311143
##      gdpPercap
## 1     974.5803
## 2    5937.0295
## 3    6223.3675
## 4    4797.2313
## 5   12779.3796
## 6   34435.3674
## 7   36126.4927
## 8   29796.0483
## 9    1391.2538
## 10  33692.6051
## 11   1441.2849
## 12   3822.1371
## 13   7446.2988
## 14  12569.8518
## 15   9065.8008
## 16  10680.7928
## 17   1217.0330
## 18    430.0707
## 19   1713.7787
## 20   2042.0952
## 21  36319.2350
## 22    706.0165
## 23   1704.0637
## 24  13171.6388
## 25   4959.1149
## 26   7006.5804
## 27    986.1479
## 28    277.5519
## 29   3632.5578
## 30   9645.0614
## 31   1544.7501
## 32  14619.2227
## 33   8948.1029
## 34  22833.3085
## 35  35278.4187
## 36   2082.4816
## 37   6025.3748
## 38   6873.2623
## 39   5581.1810
## 40   5728.3535
## 41  12154.0897
## 42    641.3695
## 43    690.8056
## 44  33207.0844
## 45  30470.0167
## 46  13206.4845
## 47    752.7497
## 48  32170.3744
## 49   1327.6089
## 50  27538.4119
## 51   5186.0500
## 52    942.6542
## 53    579.2317
## 54   1201.6372
## 55   3548.3308
## 56  39724.9787
## 57  18008.9444
## 58  36180.7892
## 59   2452.2104
## 60   3540.6516
## 61  11605.7145
## 62   4471.0619
## 63  40675.9964
## 64  25523.2771
## 65  28569.7197
## 66   7320.8803
## 67  31656.0681
## 68   4519.4612
## 69   1463.2493
## 70   1593.0655
## 71  23348.1397
## 72  47306.9898
## 73  10461.0587
## 74   1569.3314
## 75    414.5073
## 76  12057.4993
## 77   1044.7701
## 78    759.3499
## 79  12451.6558
## 80   1042.5816
## 81   1803.1515
## 82  10956.9911
## 83  11977.5750
## 84   3095.7723
## 85   9253.8961
## 86   3820.1752
## 87    823.6856
## 88    944.0000
## 89   4811.0604
## 90   1091.3598
## 91  36797.9333
## 92  25185.0091
## 93   2749.3210
## 94    619.6769
## 95   2013.9773
## 96  49357.1902
## 97  22316.1929
## 98   2605.9476
## 99   9809.1856
## 100  4172.8385
## 101  7408.9056
## 102  3190.4810
## 103 15389.9247
## 104 20509.6478
## 105 19328.7090
## 106  7670.1226
## 107 10808.4756
## 108   863.0885
## 109  1598.4351
## 110 21654.8319
## 111  1712.4721
## 112  9786.5347
## 113   862.5408
## 114 47143.1796
## 115 18678.3144
## 116 25768.2576
## 117   926.1411
## 118  9269.6578
## 119 28821.0637
## 120  3970.0954
## 121  2602.3950
## 122  4513.4806
## 123 33859.7484
## 124 37506.4191
## 125  4184.5481
## 126 28718.2768
## 127  1107.4822
## 128  7458.3963
## 129   882.9699
## 130 18008.5092
## 131  7092.9230
## 132  8458.2764
## 133  1056.3801
## 134 33203.2613
## 135 42951.6531
## 136 10611.4630
## 137 11415.8057
## 138  2441.5764
## 139  3025.3498
## 140  2280.7699
## 141  1271.2116
## 142   469.7093

Part 3 Visualization design

Here is an old graph from assignment 3. It looks at the weighted mean life expectancy for continents over time for the gapminder data set.

gapminder %>% 
 group_by(continent, year) %>%
 summarise(mean_lifeExp_weighted = weighted.mean(lifeExp, pop)) %>% 
 ggplot(aes(year, mean_lifeExp_weighted))+
  geom_point(aes(colour = continent))

What can be improved? 1) We can add a trend line so we can estimate what is happening between years. 2) We can change the axis titles and add a graph title. 3) We can re-order the factor levels so the legend is presented in logical order. 4) We can change the scale of the axes to show every 5 years of life. 5) We can change the graphs theme.

gapminder %>% 
 group_by(continent, year) %>%
 summarise(mean_lifeExp_weighted = weighted.mean(lifeExp, pop)) %>% 
 ggplot(aes(year, mean_lifeExp_weighted, color = fct_reorder2(continent, year, mean_lifeExp_weighted)))+
  geom_point(aes(colour = continent))+
  geom_line(aes(colour = continent))+
  scale_y_continuous(breaks=5*(1:17))+
  xlab("Year")+
    ylab("Weighted Mean Life Expectancy")+
  labs(color = "Continent")+
  ggtitle("Weighted Mean Life Expectancy vs. Time (Years)")+
  theme_minimal()

**Let’s try a different graph and also use plotly to make it more interactive.*Now we can make an interactive plot using plotly.**

Here is the original graph:

gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent)

Here is the cleaner version.

gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent,nrow = 5, ncol = 1)+
  scale_y_continuous(breaks=10*(1:9))+ #change the scale
  xlab("Year")+ #change the titles
  ylab("Life Expectancy")+
  ggtitle("Country life expectancy over time")+
  scale_colour_discrete(name  ="CountrylLife expectancy > worldwide median?")+ #make the legend clearer
  theme(legend.position="right") #select legend position

Now, let’s make this into a plotly graph.

#First, let's save the above graph into "plotly1"
plotly1 <- gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent,nrow = 5, ncol = 1)+
  scale_y_continuous(breaks=10*(1:9))+ 
  xlab("Year")+
  ylab("Life Expectancy")+
  ggtitle("Country life expectancy over time")+
  scale_colour_discrete(name  ="CountrylLife expectancy > worldwide median?")+
  theme(legend.position="right") 

#Now open plotly package.
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

#GEnerating plotly graph.
ggplotly(plotly1)

**We do notice that plotly has some nice features. However, visualization is not as good. The legend is cut off. We can see if moving the legend elsewhere will improve this visualization.

plotly2 <- gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent,nrow = 5, ncol = 1)+
  scale_y_continuous(breaks=10*(1:9))+ 
  xlab("Year")+
  ylab("Life Expectancy")+
  ggtitle("Country life expectancy over time")+
  scale_colour_discrete(name  ="CountrylLife expectancy > worldwide median?")+
  theme(legend.position="bottom") 

#GEnerating plotly graph.
ggplotly(plotly2)

Hmmmm. It appears plotly dose not have all the aesthetic options that ggplot does. Anyways, let’s save this as a local html file.

ggplotly(plotly1) %>% 
  htmlwidgets::saveWidget("plotly1")

Let’s save this plot using ggsave(). This will generate an image file where we can specify certain aspects such as dimension and resolution.

plotly1

ggsave("plotly1.png", width = 20, height = 20, units = "cm", dpi = 300)